
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


df = pd.read_excel('dataset.xlsx')


pd.set_option('display.max_row', 111)
pd.set_option('display.max_column', 111)

df


df.shape

(5644, 111)


df.dtypes.value_counts()

float64    70
object     37
int64       4
dtype: int64


plt.figure(figsize=(20,10))
sns.heatmap(df.isna())

<AxesSubplot: >


(df.isna().sum()/df.shape[0]).sort_values(ascending=True)

Patient ID                                               0.000000
Patient age quantile                                     0.000000
SARS-Cov-2 exam result                                   0.000000
Patient addmited to regular ward (1=yes, 0=no)           0.000000
Patient addmited to semi-intensive unit (1=yes, 0=no)    0.000000
Patient addmited to intensive care unit (1=yes, 0=no)    0.000000
Influenza B                                              0.760099
Respiratory Syncytial Virus                              0.760099
Influenza A                                              0.760099
Rhinovirus/Enterovirus                                   0.760454
Inf A H1N1 2009                                          0.760454
CoronavirusOC43                                          0.760454
Coronavirus229E                                          0.760454
Parainfluenza 4                                          0.760454
Adenovirus                                               0.760454
Chlamydophila pneumoniae                                 0.760454
Parainfluenza 3                                          0.760454
Coronavirus HKU1                                         0.760454
CoronavirusNL63                                          0.760454
Parainfluenza 1                                          0.760454
Bordetella pertussis                                     0.760454
Parainfluenza 2                                          0.760454
Metapneumovirus                                          0.760454
Influenza A, rapid test                                  0.854713
Influenza B, rapid test                                  0.854713
Hemoglobin                                               0.893161
Hematocrit                                               0.893161
Red blood cell distribution width (RDW)                  0.893338
Platelets                                                0.893338
Mean corpuscular volume (MCV)                            0.893338
Eosinophils                                              0.893338
Mean corpuscular hemoglobin (MCH)                        0.893338
Basophils                                                0.893338
Leukocytes                                               0.893338
Mean corpuscular hemoglobin concentration (MCHC)         0.893338
Lymphocytes                                              0.893338
Red blood Cells                                          0.893338
Monocytes                                                0.893515
Mean platelet volume                                     0.893870
Neutrophils                                              0.909107
Proteina C reativa mg/dL                                 0.910347
Creatinine                                               0.924876
Urea                                                     0.929660
Potassium                                                0.934266
Sodium                                                   0.934444
Strepto A                                                0.941176
Aspartate transaminase                                   0.959957
Alanine transaminase                                     0.960135
Serum Glucose                                            0.963147
Total Bilirubin                                          0.967753
Direct Bilirubin                                         0.967753
Indirect Bilirubin                                       0.967753
Gamma-glutamyltransferase                                0.972892
Alkaline phosphatase                                     0.974486
HCO3 (venous blood gas analysis)                         0.975904
pH (venous blood gas analysis)                           0.975904
Total CO2 (venous blood gas analysis)                    0.975904
Base excess (venous blood gas analysis)                  0.975904
pO2 (venous blood gas analysis)                          0.975904
pCO2 (venous blood gas analysis)                         0.975904
Hb saturation (venous blood gas analysis)                0.975904
International normalized ratio (INR)                     0.976435
Creatine phosphokinase (CPK)                             0.981573
Lactic Dehydrogenase                                     0.982105
Myeloblasts                                              0.982814
Myelocytes                                               0.982814
Metamyelocytes                                           0.982814
Promyelocytes                                            0.982814
Rods #                                                   0.982814
Segmented                                                0.982814
Relationship (Patient/Normal)                            0.983877
Urine - Crystals                                         0.987597
Urine - Color                                            0.987597
Urine - Yeasts                                           0.987597
Urine - Red blood cells                                  0.987597
Urine - Leukocytes                                       0.987597
Urine - Density                                          0.987597
Urine - Bile pigments                                    0.987597
Urine - Hemoglobin                                       0.987597
Urine - pH                                               0.987597
Urine - Aspect                                           0.987597
Urine - Urobilinogen                                     0.987775
Urine - Granular cylinders                               0.987775
Urine - Hyaline cylinders                                0.988129
Urine - Protein                                          0.989369
Urine - Esterase                                         0.989369
Urine - Ketone Bodies                                    0.989901
Ionized calcium                                          0.991141
Magnesium                                                0.992913
ctO2 (arterial blood gas analysis)                       0.995216
Hb saturation (arterial blood gases)                     0.995216
pH (arterial blood gas analysis)                         0.995216
Arterial Lactic Acid                                     0.995216
Total CO2 (arterial blood gas analysis)                  0.995216
pCO2 (arterial blood gas analysis)                       0.995216
HCO3 (arterial blood gas analysis)                       0.995216
pO2 (arterial blood gas analysis)                        0.995216
Base excess (arterial blood gas analysis)                0.995216
Ferritin                                                 0.995925
Arteiral Fio2                                            0.996456
Phosphor                                                 0.996456
Albumin                                                  0.997697
Lipase dosage                                            0.998583
Vitamin B12                                              0.999468
Urine - Nitrite                                          0.999823
Fio2 (venous blood gas analysis)                         0.999823
Partial thromboplastin time (PTT)                        1.000000
Urine - Sugar                                            1.000000
Mycoplasma pneumoniae                                    1.000000
D-Dimer                                                  1.000000
Prothrombin time (PT), Activity                          1.000000
dtype: float64


#Suppression des colonnes inutiles ayant plus de 90% de NaN
df = df[df.columns[df.isna().sum()/df.shape[0] < 0.9]]


sns.heatmap(df.isna())

<AxesSubplot: >


#Suppression de la colonne Patient ID
df = df.drop('Patient ID', axis=1)

df


#Pourcentage de cas de SARS-COV-2
df['SARS-Cov-2 exam result'].value_counts(normalize=True)

negative    0.901134
positive    0.098866
Name: SARS-Cov-2 exam result, dtype: float64


for col in df.select_dtypes('float'):
    print(col)

Hematocrit
Hemoglobin
Platelets
Mean platelet volume 
Red blood Cells
Lymphocytes
Mean corpuscular hemoglobin concentration (MCHC)
Leukocytes
Basophils
Mean corpuscular hemoglobin (MCH)
Eosinophils
Mean corpuscular volume (MCV)
Monocytes
Red blood cell distribution width (RDW)


for col in df.select_dtypes('float'):
    plt.figure()
    sns.histplot(df[col], kde=True )


sns.histplot(df['Patient age quantile'], bins=20, kde=True)

<AxesSubplot: xlabel='Patient age quantile', ylabel='Count'>


df['SARS-Cov-2 exam result'].unique()

array(['negative', 'positive'], dtype=object)


for col in df.select_dtypes('object'):
    print(f'{col:-<40} {df[col].unique()}')

SARS-Cov-2 exam result------------------ ['negative' 'positive']
Respiratory Syncytial Virus------------- [nan 'not_detected' 'detected']
Influenza A----------------------------- [nan 'not_detected' 'detected']
Influenza B----------------------------- [nan 'not_detected' 'detected']
Parainfluenza 1------------------------- [nan 'not_detected' 'detected']
CoronavirusNL63------------------------- [nan 'not_detected' 'detected']
Rhinovirus/Enterovirus------------------ [nan 'detected' 'not_detected']
Coronavirus HKU1------------------------ [nan 'not_detected' 'detected']
Parainfluenza 3------------------------- [nan 'not_detected' 'detected']
Chlamydophila pneumoniae---------------- [nan 'not_detected' 'detected']
Adenovirus------------------------------ [nan 'not_detected' 'detected']
Parainfluenza 4------------------------- [nan 'not_detected' 'detected']
Coronavirus229E------------------------- [nan 'not_detected' 'detected']
CoronavirusOC43------------------------- [nan 'not_detected' 'detected']
Inf A H1N1 2009------------------------- [nan 'not_detected' 'detected']
Bordetella pertussis-------------------- [nan 'not_detected' 'detected']
Metapneumovirus------------------------- [nan 'not_detected' 'detected']
Parainfluenza 2------------------------- [nan 'not_detected']
Influenza B, rapid test----------------- [nan 'negative' 'positive']
Influenza A, rapid test----------------- [nan 'negative' 'positive']


for col in df.select_dtypes('object'):
    plt.figure()
    df[col].value_counts().plot.pie(autopct='%1.1f%%')


positive_df = df[df['SARS-Cov-2 exam result']=='positive']
negative_df = df[df['SARS-Cov-2 exam result']=='negative']


missing_rate = df.isna().sum() / df.shape[0]


df.columns[(missing_rate < 0.9) & (missing_rate > 0.88)]

Index(['Hematocrit', 'Hemoglobin', 'Platelets', 'Mean platelet volume ',
       'Red blood Cells', 'Lymphocytes',
       'Mean corpuscular hemoglobin concentration (MCHC)', 'Leukocytes',
       'Basophils', 'Mean corpuscular hemoglobin (MCH)', 'Eosinophils',
       'Mean corpuscular volume (MCV)', 'Monocytes',
       'Red blood cell distribution width (RDW)'],
      dtype='object')


blood_columns = df.columns[(missing_rate < 0.9) & (missing_rate > 0.88)]


viral_columns = df.columns[(missing_rate < 0.88) & (missing_rate > 0.75)]


for col in blood_columns:
    plt.figure()
    sns.displot(positive_df[col], label='positive', kde=True)
    sns.displot(negative_df[col], label='negative', kde=True)
    plt.legend()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\axisgrid.py:447: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig = plt.figure(figsize=figsize)

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>


sns.countplot(x='Patient age quantile', hue='SARS-Cov-2 exam result', data=df)

<AxesSubplot: xlabel='Patient age quantile', ylabel='count'>


pd.crosstab(df['SARS-Cov-2 exam result'], df['Influenza A'])


for col in viral_columns:
    plt.figure()
    sns.heatmap(pd.crosstab(df['SARS-Cov-2 exam result'], df[col]), annot=True, fmt='d')


sns.pairplot(df[blood_columns])

<seaborn.axisgrid.PairGrid at 0x2349deae280>


sns.heatmap(df[blood_columns].corr())

<AxesSubplot: >


sns.clustermap(df[blood_columns].corr())

<seaborn.matrix.ClusterGrid at 0x234ac128100>


for col in blood_columns:
    plt.figure()
    sns.lmplot(x='Patient age quantile', y=col, hue='SARS-Cov-2 exam result', data=df)

C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\695644951.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  plt.figure()

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>


df.corr()['Patient age quantile'].sort_values()

Leukocytes                                              -0.166386
Platelets                                               -0.158683
Lymphocytes                                             -0.125935
Mean corpuscular hemoglobin concentration (MCHC)        -0.124671
Red blood Cells                                         -0.037510
Patient addmited to intensive care unit (1=yes, 0=no)   -0.035772
Patient addmited to semi-intensive unit (1=yes, 0=no)    0.015736
Eosinophils                                              0.022085
Patient addmited to regular ward (1=yes, 0=no)           0.046166
Monocytes                                                0.050962
Hemoglobin                                               0.060320
Hematocrit                                               0.096808
Basophils                                                0.107525
Mean platelet volume                                     0.119449
Red blood cell distribution width (RDW)                  0.166429
Mean corpuscular hemoglobin (MCH)                        0.197394
Mean corpuscular volume (MCV)                            0.281655
Patient age quantile                                     1.000000
Name: Patient age quantile, dtype: float64


pd.crosstab(df['Influenza A'], df['Influenza A, rapid test'])


pd.crosstab(df['Influenza B'], df['Influenza B, rapid test'])


np.sum(df[viral_columns[:-2]] == "detected", axis=1).plot()

<AxesSubplot: >


np.sum(df[viral_columns[:-2]] == "detected", axis=1) >= 1

0       False
1        True
2       False
3       False
4        True
        ...  
5639    False
5640    False
5641    False
5642    False
5643    False
Length: 5644, dtype: bool


df['est malade'] = np.sum(df[viral_columns[:-2]] == "detected", axis=1) >= 1


malade_df = df[df['est malade'] == True]
non_malade_df = df[df['est malade'] == False]


for col in blood_columns:
    plt.figure()
    sns.distplot(malade_df[col], label='malade')
    sns.distplot(non_malade_df[col], label='non malade')
    plt.legend()

C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(malade_df[col], label='malade')
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2167010223.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(non_malade_df[col], label='non malade')


def hospitalisation(df):
    if df['Patient addmited to regular ward (1=yes, 0=no)'] == 1:
        return 'surveillance'
    elif df['Patient addmited to semi-intensive unit (1=yes, 0=no)'] == 1:
        return 'soins semi-intensives'
    elif df['Patient addmited to intensive care unit (1=yes, 0=no)'] == 1:
        return 'soins intensifs'
    else:
        return 'inconnu'


df['statut'] = df.apply(hospitalisation, axis=1)


df.head()


for col in blood_columns:
    plt.figure()
    for cat in df['statut'].unique():
        sns.distplot(df[df['statut']==cat][col], label=cat)
    plt.legend()

C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)
C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\4182288631.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[df['statut']==cat][col], label=cat)


blood_columns

Index(['Hematocrit', 'Hemoglobin', 'Platelets', 'Mean platelet volume ',
       'Red blood Cells', 'Lymphocytes',
       'Mean corpuscular hemoglobin concentration (MCHC)', 'Leukocytes',
       'Basophils', 'Mean corpuscular hemoglobin (MCH)', 'Eosinophils',
       'Mean corpuscular volume (MCV)', 'Monocytes',
       'Red blood cell distribution width (RDW)'],
      dtype='object')


viral_columns

Index(['Respiratory Syncytial Virus', 'Influenza A', 'Influenza B',
       'Parainfluenza 1', 'CoronavirusNL63', 'Rhinovirus/Enterovirus',
       'Coronavirus HKU1', 'Parainfluenza 3', 'Chlamydophila pneumoniae',
       'Adenovirus', 'Parainfluenza 4', 'Coronavirus229E', 'CoronavirusOC43',
       'Inf A H1N1 2009', 'Bordetella pertussis', 'Metapneumovirus',
       'Parainfluenza 2', 'Influenza B, rapid test',
       'Influenza A, rapid test'],
      dtype='object')


sns.heatmap(df.isna())

<AxesSubplot: >


df.dropna().count()

Patient age quantile                                     99
SARS-Cov-2 exam result                                   99
Patient addmited to regular ward (1=yes, 0=no)           99
Patient addmited to semi-intensive unit (1=yes, 0=no)    99
Patient addmited to intensive care unit (1=yes, 0=no)    99
Hematocrit                                               99
Hemoglobin                                               99
Platelets                                                99
Mean platelet volume                                     99
Red blood Cells                                          99
Lymphocytes                                              99
Mean corpuscular hemoglobin concentration (MCHC)         99
Leukocytes                                               99
Basophils                                                99
Mean corpuscular hemoglobin (MCH)                        99
Eosinophils                                              99
Mean corpuscular volume (MCV)                            99
Monocytes                                                99
Red blood cell distribution width (RDW)                  99
Respiratory Syncytial Virus                              99
Influenza A                                              99
Influenza B                                              99
Parainfluenza 1                                          99
CoronavirusNL63                                          99
Rhinovirus/Enterovirus                                   99
Coronavirus HKU1                                         99
Parainfluenza 3                                          99
Chlamydophila pneumoniae                                 99
Adenovirus                                               99
Parainfluenza 4                                          99
Coronavirus229E                                          99
CoronavirusOC43                                          99
Inf A H1N1 2009                                          99
Bordetella pertussis                                     99
Metapneumovirus                                          99
Parainfluenza 2                                          99
Influenza B, rapid test                                  99
Influenza A, rapid test                                  99
est malade                                               99
statut                                                   99
dtype: int64


df[blood_columns].count()

Hematocrit                                          603
Hemoglobin                                          603
Platelets                                           602
Mean platelet volume                                599
Red blood Cells                                     602
Lymphocytes                                         602
Mean corpuscular hemoglobin concentration (MCHC)    602
Leukocytes                                          602
Basophils                                           602
Mean corpuscular hemoglobin (MCH)                   602
Eosinophils                                         602
Mean corpuscular volume (MCV)                       602
Monocytes                                           601
Red blood cell distribution width (RDW)             602
dtype: int64


df[viral_columns].count()

Respiratory Syncytial Virus    1354
Influenza A                    1354
Influenza B                    1354
Parainfluenza 1                1352
CoronavirusNL63                1352
Rhinovirus/Enterovirus         1352
Coronavirus HKU1               1352
Parainfluenza 3                1352
Chlamydophila pneumoniae       1352
Adenovirus                     1352
Parainfluenza 4                1352
Coronavirus229E                1352
CoronavirusOC43                1352
Inf A H1N1 2009                1352
Bordetella pertussis           1352
Metapneumovirus                1352
Parainfluenza 2                1352
Influenza B, rapid test         820
Influenza A, rapid test         820
dtype: int64


df1 = df[viral_columns[:-2]]
df1['covid']= df['SARS-Cov-2 exam result']
df1.dropna()['covid'].value_counts(normalize=True)

C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\2316125735.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1['covid']= df['SARS-Cov-2 exam result']

negative    0.91716
positive    0.08284
Name: covid, dtype: float64


df2 = df[blood_columns[:-2]]
df2['covid']= df['SARS-Cov-2 exam result']
df2.dropna()['covid'].value_counts(normalize=True)

C:\Users\ILIAS SABANI\AppData\Local\Temp\ipykernel_9916\3602602215.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df2['covid']= df['SARS-Cov-2 exam result']

negative    0.864775
positive    0.135225
Name: covid, dtype: float64


from scipy.stats import ttest_ind


positive_df.shape

(558, 38)


negative_df.shape

(5086, 38)


balanced_neg = negative_df.sample(positive_df.shape[0])


def t_test(col):
    alpha=0.02
    stat, p = ttest_ind(balanced_neg[col].dropna(), positive_df[col].dropna())
    if p < alpha:
        return 'HO Rejetée'
    else:
        return 0


for col in blood_columns:
    print(f'{col :-<50} {t_test(col)}')

Hematocrit---------------------------------------- 0
Hemoglobin---------------------------------------- 0
Platelets----------------------------------------- HO Rejetée
Mean platelet volume ----------------------------- 0
Red blood Cells----------------------------------- 0
Lymphocytes--------------------------------------- 0
Mean corpuscular hemoglobin concentration (MCHC)-- 0
Leukocytes---------------------------------------- HO Rejetée
Basophils----------------------------------------- HO Rejetée
Mean corpuscular hemoglobin (MCH)----------------- 0
Eosinophils--------------------------------------- HO Rejetée
Mean corpuscular volume (MCV)--------------------- 0
Monocytes----------------------------------------- HO Rejetée
Red blood cell distribution width (RDW)----------- 0


data = df.copy()


data.head()


missing_rate = data.isna().sum()/data.shape[0]


blood_columns = list(data.columns[(missing_rate < 0.9)&(missing_rate > 0.88)])
viral_columns = list(data.columns[(missing_rate < 0.8)&(missing_rate > 0.75)])


key_columns = ['Patient age quantile', 'SARS-Cov-2 exam result']


data = data[key_columns + blood_columns + viral_columns]


data


from sklearn.model_selection import *


trainset, testset = train_test_split(data, test_size=0.2, random_state=0)


trainset['SARS-Cov-2 exam result'].value_counts()

negative    4068
positive     447
Name: SARS-Cov-2 exam result, dtype: int64


testset['SARS-Cov-2 exam result'].value_counts()

negative    1018
positive     111
Name: SARS-Cov-2 exam result, dtype: int64


def encodage(data):
    code = {'positive':1,
            'negative':0,
            'detected':1,
            'not_detected':0}

    for col in data.select_dtypes('object').columns:
        data.loc[:,col] = data[col].map(code)
    
    return data


def feature_engineering(data):
    data['est malade'] = data[viral_columns].sum(axis=1) >= 1
    data = data.drop(viral_columns, axis=1)
    return data


def imputation(data):
    #data['is na'] = (data['Parainfluenza 3'].isna()) | (data['Leukocytes'].isna())
    #data = data.fillna(-999)
    data = data.dropna(axis=0)
    return data


def preprocessing(data):
    data = encodage(data)
    data = feature_engineering(data)
    data = imputation(data)
    
    X = data.drop('SARS-Cov-2 exam result', axis=1)
    y = data['SARS-Cov-2 exam result']
    
    print(y.value_counts())
    
    return X, y


X_train, y_train = preprocessing(trainset)

0    422
1     65
Name: SARS-Cov-2 exam result, dtype: int64


X_test, y_test = preprocessing(testset)

0    95
1    16
Name: SARS-Cov-2 exam result, dtype: int64


from sklearn.tree import *
from sklearn.ensemble import *
from sklearn.pipeline import *
from sklearn.feature_selection import *
from sklearn.preprocessing import *
from sklearn.decomposition import *
from sklearn.svm import *
from sklearn.neighbors import *


preprocessor = make_pipeline(PolynomialFeatures(2), SelectKBest(f_classif, k=10))


RandomForest = make_pipeline(preprocessor, RandomForestClassifier(random_state=0))
AdaBoost = make_pipeline(preprocessor, AdaBoostClassifier(random_state=0))
SVM = make_pipeline(preprocessor, StandardScaler(), SVC(random_state=0))
KNN = make_pipeline(preprocessor, StandardScaler(), KNeighborsClassifier())


list_of_models = {'RandomForest': RandomForest,
                  'AdaBoost': AdaBoost,
                  'SVM': SVM,
                  'KNN': KNN
                 }


for name, model in list_of_models.items():
    print(name)
    evaluation(model)

RandomForest
[[91  4]
 [11  5]]
              precision    recall  f1-score   support

           0       0.89      0.96      0.92        95
           1       0.56      0.31      0.40        16

    accuracy                           0.86       111
   macro avg       0.72      0.64      0.66       111
weighted avg       0.84      0.86      0.85       111

AdaBoost
[[91  4]
 [ 9  7]]
              precision    recall  f1-score   support

           0       0.91      0.96      0.93        95
           1       0.64      0.44      0.52        16

    accuracy                           0.88       111
   macro avg       0.77      0.70      0.73       111
weighted avg       0.87      0.88      0.87       111

SVM
[[92  3]
 [10  6]]
              precision    recall  f1-score   support

           0       0.90      0.97      0.93        95
           1       0.67      0.38      0.48        16

    accuracy                           0.88       111
   macro avg       0.78      0.67      0.71       111
weighted avg       0.87      0.88      0.87       111

KNN
[[88  7]
 [ 8  8]]
              precision    recall  f1-score   support

           0       0.92      0.93      0.92        95
           1       0.53      0.50      0.52        16

    accuracy                           0.86       111
   macro avg       0.72      0.71      0.72       111
weighted avg       0.86      0.86      0.86       111


from sklearn.metrics import *
from sklearn.model_selection import *


def evaluation(model):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    print(confusion_matrix(y_test, y_pred))
    print(classification_report(y_test, y_pred))
    
    N, train_score, val_score = learning_curve(model, X_train, y_train,
                                              cv=4, scoring='f1', 
                                               train_sizes=np.linspace(0.1,1,10))
    
    plt.figure(figsize=(12,8))
    plt.plot(N,train_score.mean(axis=1), label='train score')
    plt.plot(N,val_score.mean(axis=1), label='validation score')
    plt.legend()


evaluation(model)

[[91  4]
 [11  5]]
              precision    recall  f1-score   support

           0       0.89      0.96      0.92        95
           1       0.56      0.31      0.40        16

    accuracy                           0.86       111
   macro avg       0.72      0.64      0.66       111
weighted avg       0.84      0.86      0.85       111


pd.DataFrame(model.feature_importances_, index = X_train.columns).plot.bar()

<AxesSubplot: >


from sklearn.model_selection import *

SVM

Pipeline(steps=[('pipeline',
                 Pipeline(steps=[('polynomialfeatures', PolynomialFeatures()),
                                 ('selectkbest', SelectKBest())])),
                ('standardscaler', StandardScaler()),
                ('svc', SVC(random_state=0))])


hyper_params = {'svc__gamma' : [1e-3, 1e-4],
               'svc__C': [1, 10, 100, 1000],
               'pipeline__polynomialfeatures__degree':[2,3,4,5],
                'pipeline__selectkbest__k':range(40,100)
               }


grid = RandomizedSearchCV(SVM, hyper_params, scoring='recall', cv=4, n_iter=50)

grid.fit(X_train, y_train)

print(grid.best_params_)

y_pred = grid.predict(X_test)

print(classification_report(y_test, y_pred))

{'svc__gamma': 0.001, 'svc__C': 1000, 'pipeline__selectkbest__k': 56, 'pipeline__polynomialfeatures__degree': 3}
              precision    recall  f1-score   support

           0       0.92      0.93      0.92        95
           1       0.53      0.50      0.52        16

    accuracy                           0.86       111
   macro avg       0.72      0.71      0.72       111
weighted avg       0.86      0.86      0.86       111


evaluation(grid.best_estimator_)

[[88  7]
 [ 8  8]]
              precision    recall  f1-score   support

           0       0.92      0.93      0.92        95
           1       0.53      0.50      0.52        16

    accuracy                           0.86       111
   macro avg       0.72      0.71      0.72       111
weighted avg       0.86      0.86      0.86       111


from sklearn.metrics import *


precision, recall, threshold = precision_recall_curve(y_test, grid.best_estimator_.decision_function(X_test))


plt.plot(threshold, precision[:-1], label='precision')
plt.plot(threshold, recall[:-1], label='recall')
plt.legend()

<matplotlib.legend.Legend at 0x234b59aaaf0>


def model_final(model, X, threshold=0):
    return model.decision_function(X) > threshold


y_pred = model_final(grid.best_estimator_, X_test, threshold=0)


f1_score(y_test, y_pred)

0.14285714285714285


recall_score(y_test, y_pred)

0.5

SARS-COV-2 Data Analysis and Machine Learning¶

Importing Libraries¶

Importing Data¶

Data Cleaning and Preprocessing¶

Analyse du fond¶

1. Visualisation initiale - Elimination des colonnes inutiles¶

Examen de la colonne Target¶

Histogrames des variables continues¶

Variables discrètes¶

Relation Target/Variable¶

Création de sous-ensembles positifs et négatifs¶

Création des ensembles Blood et Viral¶

Visualisation de la relation Target/Blood¶

Visualisation de la relation Target/Age¶

Visualisation de la relation Target/Viral¶

Aanalyse un peu plus avancée¶

Relation Variables / Variables¶

Relations Blood/Blood¶

Relations Blood/Age¶

Relation Viral/Viral¶

Relation Maladie/Blood¶

Relation Hospitalisation / Maladie¶

NaN Analysis¶

Hypotheses nulle (HO)¶

T-Test¶

Train Test - Nettoyage - Encodage¶

Encodage¶

Modelisation (DecisionTreeClassifier)¶

Procédure d'évalution (f1_score, confusion_matrix, classification_report, learning_curve)¶

Optimisation¶

Precision Recall Curve¶

	Patient ID	Patient age quantile	SARS-Cov-2 exam result	Patient addmited to regular ward (1=yes, 0=no)	Patient addmited to semi-intensive unit (1=yes, 0=no)	Patient addmited to intensive care unit (1=yes, 0=no)	Hematocrit	Hemoglobin	Platelets	Mean platelet volume	Red blood Cells	Lymphocytes	Mean corpuscular hemoglobin concentration (MCHC)	Leukocytes	Basophils	Mean corpuscular hemoglobin (MCH)	Eosinophils	Mean corpuscular volume (MCV)	Monocytes	Red blood cell distribution width (RDW)	Serum Glucose	Respiratory Syncytial Virus	Influenza A	Influenza B	Parainfluenza 1	CoronavirusNL63	Rhinovirus/Enterovirus	Mycoplasma pneumoniae	Coronavirus HKU1	Parainfluenza 3	Chlamydophila pneumoniae	Adenovirus	Parainfluenza 4	Coronavirus229E	CoronavirusOC43	Inf A H1N1 2009	Bordetella pertussis	Metapneumovirus	Parainfluenza 2	Neutrophils	Urea	Proteina C reativa mg/dL	Creatinine	Potassium	Sodium	Influenza B, rapid test	Influenza A, rapid test	Alanine transaminase	Aspartate transaminase	Gamma-glutamyltransferase	Total Bilirubin	Direct Bilirubin	Indirect Bilirubin	Alkaline phosphatase	Ionized calcium	Strepto A	Magnesium	pCO2 (venous blood gas analysis)	Hb saturation (venous blood gas analysis)	Base excess (venous blood gas analysis)	pO2 (venous blood gas analysis)	Fio2 (venous blood gas analysis)	Total CO2 (venous blood gas analysis)	pH (venous blood gas analysis)	HCO3 (venous blood gas analysis)	Rods #	Segmented	Promyelocytes	Metamyelocytes	Myelocytes	Myeloblasts	Urine - Esterase	Urine - Aspect	Urine - pH	Urine - Hemoglobin	Urine - Bile pigments	Urine - Ketone Bodies	Urine - Nitrite	Urine - Density	Urine - Urobilinogen	Urine - Protein	Urine - Sugar	Urine - Leukocytes	Urine - Crystals	Urine - Red blood cells	Urine - Hyaline cylinders	Urine - Granular cylinders	Urine - Yeasts	Urine - Color	Partial thromboplastin time (PTT)	Relationship (Patient/Normal)	International normalized ratio (INR)	Lactic Dehydrogenase	Prothrombin time (PT), Activity	Vitamin B12	Creatine phosphokinase (CPK)	Ferritin	Arterial Lactic Acid	Lipase dosage	D-Dimer	Albumin	Hb saturation (arterial blood gases)	pCO2 (arterial blood gas analysis)	Base excess (arterial blood gas analysis)	pH (arterial blood gas analysis)	Total CO2 (arterial blood gas analysis)	HCO3 (arterial blood gas analysis)	pO2 (arterial blood gas analysis)	Arteiral Fio2	Phosphor	ctO2 (arterial blood gas analysis)
0	44477f75e8169d2	13	negative	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	126e9dd13932f68	17	negative	0	0	0	0.236515	-0.022340	-0.517413	0.010677	0.102004	0.318366	-0.950790	-0.094610	-0.223767	-0.292269	1.482158	0.166192	0.357547	-0.625073	-0.140648	not_detected	not_detected	not_detected	not_detected	not_detected	detected	NaN	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	-0.619086	1.198059	-0.147895	2.089928	-0.305787	0.862512	negative	negative	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	a46b4402a0e5696	8	negative	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	f7d619a94f97c45	5	negative	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	d9e41465789c2b5	15	negative	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	not_detected	not_detected	not_detected	not_detected	not_detected	detected	NaN	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
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5639	ae66feb9e4dc3a0	3	positive	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5640	517c2834024f3ea	17	negative	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5641	5c57d6037fe266d	4	negative	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5642	c20c44766f28291	10	negative	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	clear	5	absent	absent	absent	NaN	-0.338525	normal	absent	NaN	29000	Ausentes	-0.177169	absent	absent	absent	yellow	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5643	2697fdccbfeb7f7	19	positive	0	0	0	0.694287	0.541564	-0.906829	-0.325903	0.578024	-0.295726	-0.353319	-1.288428	-1.140144	-0.135455	-0.835508	0.025985	0.567652	-0.182790	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.380685	0.453725	-0.503570	-0.735872	-0.552949	-0.934388	NaN	NaN	-0.28361	0.108761	-0.420454	-0.480996	-0.586463	-0.278654	-0.243405	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.420204	NaN	NaN	-0.343291	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Patient age quantile	SARS-Cov-2 exam result	Hematocrit	Hemoglobin	Platelets	Mean platelet volume	Red blood Cells	Lymphocytes	Mean corpuscular hemoglobin concentration (MCHC)	Leukocytes	Basophils	Mean corpuscular hemoglobin (MCH)	Eosinophils	Mean corpuscular volume (MCV)	Monocytes	Red blood cell distribution width (RDW)	Respiratory Syncytial Virus	Influenza A	Influenza B	Parainfluenza 1	CoronavirusNL63	Rhinovirus/Enterovirus	Coronavirus HKU1	Parainfluenza 3	Chlamydophila pneumoniae	Adenovirus	Parainfluenza 4	Coronavirus229E	CoronavirusOC43	Inf A H1N1 2009	Bordetella pertussis	Metapneumovirus	Parainfluenza 2	Influenza B, rapid test	Influenza A, rapid test	est malade	statut
0	13	negative	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	False	inconnu
1	17	negative	0.236515	-0.02234	-0.517413	0.010677	0.102004	0.318366	-0.95079	-0.09461	-0.223767	-0.292269	1.482158	0.166192	0.357547	-0.625073	not_detected	not_detected	not_detected	not_detected	not_detected	detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	negative	negative	True	inconnu
2	8	negative	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	False	inconnu
3	5	negative	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	False	inconnu
4	15	negative	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	not_detected	not_detected	not_detected	not_detected	not_detected	detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	not_detected	NaN	NaN	True	inconnu